Loyalty Finance Part 1: Liability Lessons for A Smarter Loyalty Program

Managed well, a customer loyalty rewards program (think airline miles and hotel points) can have a major positive impact on a company's bottom line. As the first of a three-part blog series, this post explores new practices in loyalty finance and how they're reshaping programs rewards programs.

Even large, sophisticated programs often miss the opportunity to manage their program as a financial asset — instead, finance managers often focus on it as a liability on their books. The goal is to get a loyalty program to the point where you canpredict, monitor and optimize its valuenet of this liability — a challenging proposition without the right forecasting tools.

Before considering the solutions to program value and finance, here are a few trends and details to keep in mind:

New Standards

Loyalty Program accounting standards have recently changed.

We could spend hours reviewing recent updates or dissecting macro trends and the governing bodies that make them happen.

But what really matters to program accountants is this: loyalty program reward transaction price is allocated between theproduct or service soldand the loyalty reward obligationbased on their respective standalone values.

Take an airline reward miles program, for example: when a member books a flight, they're purchasing two things:

A plane ticket (the member is owed a seat on the plane)

Reward Miles (the member is owed redemption of those miles at a later date)

The revenue for the miles is only recognized when the member redeems them, meaning the airline is reducing revenue at time of issuance and carrying unredeemed liability on their balance sheet until the member decides to cash in. An important implication to remember is that the financial impact of redemption occurs at the time of issuing the point as well as at the time of redemption.

The percentage of all points issued across the program that will be ultimately redeemed is the loyalty program redemption rate (or ultimate redemption rate).

Of course, not every point will be redeemed in the loyalty incentive program. This results in a phenomenon known asbreakage, or the percentage of points issued that are not expected to be redeemed due to lapse or expiration. Breakage is equal to 1 - URR.

Predictive tools like KYROS deliverinsights into liability reporting and financing, helping you obtain accuracy in loyalty program redemption rate and breakage estimation — but many loyalty programs still use static, outdated methods to track and predict breakage.

Breakage Bottlenecks

So what are the outdated,home-grown methodsfor breakage estimation?

Traditional actuarial methods (like those used by insurance companies to estimate claims behavior) tend to assume that points earned in a given year will be redeemed similarly to points earned in prior years.

But what works for the insurance industry isn't nimble enough for loyalty programs, where redemption behaviors are more likely to change over time (after all, driving engagement is the goal!). This is particularly true the more successful your program is.

In this environment, these traditional actuarial methods tend to understate loyalty program redemption rates, ultimately meaning that more redemptions will occur than were expected.

This puts company finances at risk of not having enough deferred revenue to cover the costs of fulfilling redemptions, reducing income during the period. KYROS provides deeper insights into predicted loyalty program redemption rates, enabling smarter, more reliable financial forecasting for your balance sheet and income statement.

Another common breakage prediction tactic, vintage-based models aggregate members by the month in which they join a loyalty program and track cumulative redeemed points divided by cumulative earned points over time.

Though they produce results similar to those of actuarial models, they track only the percent of earned points redeemed — but those earned points are increasing at each age for a given join month. Because vintage-based models don't hold the exposure base constant, they tend to overstate URR and force companies to defer more revenue than is ultimately needed.

Relying on these relatively inaccurate estimation strategies can have a major impact on profitability down the road, exposing financial risk and income volatility. Ultimately, it means a fundamental assumption in your financial models is incorrect, potentially leading to poor business decisions.

A Better Way

Fortunately, there are better ways to estimate loyalty program redemption rates. Instead of the lagging spreadsheet based models described above, predictive member-level (or point-level) models examine billions of data points to produce a URR foreach member(or point), allowing for aggregation by various groups, which provides more insight into loyalty program member behavior.

That's where KYROS comes in.

In addition to providing managers all of the necessary data to book program liability for accounting purposes, KYROS' self-service dashboard enables loyalty accounting, finance and marketing professionals to make smarter business decisions by seeing the loyalty program as a shifting, quantifiable asset that generates value well above and beyond the costs implied by the liability.

Whether it's a quick snapshot into data trends or a more granular understanding of rewards performance, the KYROS dashboards provides unprecedented insight into the predicted future value and behavior of your members.

By Len Llaguno
Founder and managing partner of KYROS Insights. I'm an analytics nerd and recovering actuary. I use machine learning to help loyalty programs predict member behavior so they can identify their future best customers, and recognize and reward them today.